Optum – Associate Data Analyst – Scala, PySpark, Azure Cloud, AI

The Associate Data Analyst position at Optum is an opportunity for early-career technology professionals to work across data analytics, cloud computing, big data processing, and artificial intelligence. Based in Hyderabad, this role focuses on developing and enhancing data-driven algorithms and technical solutions within an Azure cloud environment. The position is particularly suited to candidates with hands-on experience in Scala and PySpark, along with a strong understanding of data analytics and modern software development practices. As part of Optum's technology ecosystem, professionals in this role will work on solutions that help transform complex business requirements into scalable technical applications. The role offers valuable exposure to enterprise-level data platforms while allowing candidates to develop expertise in cloud technologies, distributed data processing, and emerging AI capabilities.

A significant aspect of this position involves understanding business requirements and converting them into effective data and technology solutions. The Associate Data Analyst will work with business teams to understand their objectives, develop or enhance algorithms, and use technologies such as Scala, PySpark, and Microsoft Azure to implement solutions. The role also includes exploring opportunities to use Artificial Intelligence and Large Language Models (LLMs) to automate existing processes and improve project outcomes. This combination makes the position broader than a traditional data analyst role, as it involves programming, big data engineering, cloud development, automation, and AI. Candidates will also gain experience working with APIs, software development processes, and enterprise technology environments while collaborating with multiple teams to deliver reliable and valuable data solutions.

The position also provides an opportunity to contribute to innovation and continuous improvement within Optum's technology environment. Professionals will be expected to identify areas where new technologies can improve existing processes, increase automation, or enhance project deliverables. Knowledge of CI/CD and Agile methodologies is important because solutions are developed and delivered through structured and collaborative engineering practices. Strong communication skills are equally valuable, as the role requires regular interaction with business stakeholders and technical teams. For professionals interested in building careers in data engineering, data analytics, cloud computing, AI, or big data, this opportunity can provide strong practical exposure. Working with Scala, PySpark, Azure, APIs, and LLM-based technologies also gives candidates a modern technical foundation for progressing into more specialized data and AI roles.

Roles & Responsibilities

1. Develop and enhance data algorithms based on business requirements, translating functional needs into scalable technical solutions that can operate effectively within Optum's Azure-based technology environment.
2. Build data-processing applications using Scala and PySpark, applying programming and distributed-computing concepts to process large datasets and support complex analytical and business requirements.
3. Analyze business requirements by collaborating with business stakeholders and technical teams, understanding the underlying problem, and determining appropriate data, cloud, programming, or AI-based solutions.
4. Implement AI and LLM-based automation by identifying repetitive or inefficient processes where artificial intelligence and Large Language Models can improve productivity, accuracy, or overall project outcomes.
5. Work with Microsoft Azure cloud technologies to develop, enhance, and support data solutions, ensuring that applications and analytical workloads are designed appropriately for cloud environments.
6. Improve existing algorithms and project deliverables by analyzing current processes, identifying inefficiencies, and implementing technical improvements that enhance scalability, performance, accuracy, and usability.
7. Work with APIs and integrations to enable communication between applications, services, and data platforms while supporting reliable information exchange across enterprise technology systems.
8. Participate in CI/CD processes by following structured development, testing, deployment, and release practices that help deliver reliable data and analytics solutions.
9. Follow Agile development practices by participating in collaborative planning, development discussions, problem-solving sessions, reviews, and iterative delivery activities with cross-functional teams.
10. Research and introduce new technologies that can improve project capabilities, automate manual processes, enhance data solutions, or create new opportunities for AI-driven business applications.
11. Validate and troubleshoot data solutions by investigating technical issues, checking data-processing results, identifying potential problems, and implementing appropriate improvements or corrective measures.
12. Communicate technical concepts effectively with business and technical stakeholders, explaining proposed solutions, implementation approaches, limitations, and expected outcomes in a clear and understandable manner.

Requirements & Eligibility

1. Educational Qualification: Candidates should possess a relevant graduate-level qualification or equivalent professional experience. Backgrounds in computer science, information technology, engineering, mathematics, statistics, data science, or related disciplines can be particularly relevant.
2. Data Analytics Experience: Candidates should have at least 1 year of experience in data analytics and understand how data can be processed, analyzed, transformed, and used to solve practical business problems.
3. Scala Programming: Strong hands-on experience with Scala programming is required. Candidates should be comfortable developing structured applications and working with Scala-based data-processing solutions in an enterprise environment.
4. PySpark Knowledge: Practical experience with PySpark is essential. Candidates should understand distributed data processing and be capable of developing transformations and analytical workloads for large datasets.
5. Azure Cloud: Candidates should have strong practical experience with Microsoft Azure and understand how cloud-based data and analytics workloads are developed, deployed, and managed within an enterprise environment.
6. AI & LLM Knowledge: A good understanding of Artificial Intelligence and Large Language Models is expected. Exposure to generative AI, LLM applications, AI-powered automation, or related technologies can be particularly beneficial.
7. API Understanding: Candidates should understand how APIs allow different applications and services to communicate. Knowledge of API-based integrations and application-to-application data exchange will support the requirements of the position.
8. CI/CD & Agile: Candidates should be familiar with CI/CD practices and Agile methodologies, including iterative development, collaborative delivery, testing, deployment, and continuous improvement. Familiarity with tools such as Rally can be an added advantage.
9. Communication Skills: Strong verbal and written communication is important because the position involves working directly with business teams and technical stakeholders. Candidates should be able to understand business requirements and explain technical solutions clearly.
10. Analytical & Problem-Solving Skills: Candidates should demonstrate strong analytical thinking, attention to detail, and a structured approach to solving technical problems. An interest in experimenting with new technologies and identifying opportunities for innovation will be valuable in this role.

Expected Salary

For an Associate Data Analyst at Optum in Hyderabad with around 1+ years of experience, a realistic expected salary range would be approximately ₹7 lakh to ₹12 lakh per year. Candidates with stronger expertise in Scala, PySpark, Azure, AI, and LLM technologies may have the potential to command compensation toward the higher end of this range, depending on the internal level, experience, and overall compensation structure.